Multi-Level Control Systems

1966 ◽  
pp. 252-256 ◽  
Author(s):  
J. D. Pearson
Keyword(s):  
Control Systems ◽  
Level Control ◽  
Multi Level

Neural network modeling of the efficiency of response to emergency situations in a multi-level control system

2021 ◽  
Vol 92 ◽  
pp. 79-93
Author(s):  
N. G. Topolsky ◽  
◽  
S. Y. Butuzov ◽  
V. Y. Vilisov ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Systems ◽  
Hierarchical Control ◽  
Hierarchical Systems ◽  
Level Control ◽  
Emergency Situations ◽  
Expert Assessments ◽  
Multi Level ◽  
The Relationship

Introduction. It is important to have models that adequately describe the relationship between the integral indicators of the functioning of the system with the particular indicators of the lower levels of management in complex control systems, in particular in RSChS. Traditional approaches based on normative models often turn out to be untenable due to the impossibility of covering all aspects of the functioning of such systems, as well as due to the high variability of the environment and the values of the set of target indicators. Recently, adaptive machine-learning models have proven to be productive, allowing build stable and adequate models, one of the variants of which is artificial neural networks (ANN), based on the solution of inverse problems using expert estimates. The relevance of the study lies in the development of compact models that allow assessing the effectiveness of the functioning of complex multi-level control systems (RSChS) in emergency situations, developing according to complex scenarios, in which emergencies of various types can occur simultaneously. Goals and objectives. The purpose of the article is to build and test the technology for creating compact models that are adequate to the system of indicators of the functioning of hierarchically organized control systems. This goal gives rise to the task of choosing tools for constructing the necessary models and sources of initial data. Methods. The research tools include methods for analyzing hierarchical systems, mathematical statistics, machine learning methods of ANN, simulation modeling, expert assessment methods, software systems for processing statistical data. The research is based on materials from domestic and foreign publications. Results and discussion. The proposed technology for constructing a neural network model of the effectiveness of the functioning of complex hierarchical systems provides a basis for constructing dynamic models of this type, which make it possible to distribute limited financial and other resources during the operation of the system according to a complex scenario of emergency response. Conclusion. The paper presents the results of solving the problem of constructing an ANN and its corresponding nonlinear function, reflecting the relationship between the performance indicators of the lower levels of the hierarchical control system (RSChS) with the upper level. The neural network model constructed in this way can be used in the decision support system for resource management in the context of complex scenarios for the development of emergency situations. The use of expert assessments as an information basis makes it possible to take into account numerous target indicators, which are extremely difficult to take into account in other ways. Keywords: emergency situations, hierarchical control system, efficiency, artificial neural network, expert assessments

Multi-Level Control Systems

1965 ◽  
Vol 2 (2) ◽  
pp. 252-256 ◽  
Author(s):  
J.D. Pearson
Keyword(s):  
Control Systems ◽  
Level Control ◽  
Multi Level

Automation issues in multi-level control systems of petroleum refinery

2019 ◽  
Author(s):  
A. V. Zykina ◽  
O. N. Kaneva ◽  
M. Yu. Savelev ◽  
T. Yu. Fink
Keyword(s):  
Control Systems ◽  
Petroleum Refinery ◽  
Level Control ◽  
Multi Level

Optimization of Water Level Control Systems Using ANFIS and Fuzzy-PID Model

2020 ◽  
Author(s):  
Muhlasin ◽  
Budiman ◽  
Machrus Ali ◽  
Asnun Parwanti ◽  
Aji Akbar Firdaus ◽  
...  
Keyword(s):  
Water Level ◽  
Control Systems ◽  
Fuzzy Pid ◽  
Level Control

Multi-Level Control System for an Intelligent Robot that is Part of a Group

2021 ◽  
Vol 22 (11) ◽  
pp. 610-615
Author(s):  
V. I. Rubtsov ◽  
K. J. Mashkov ◽  
K. V. Konovalov
Keyword(s):  
Control System ◽  
Mobile Robotics ◽  
The State ◽  
Control Mode ◽  
Intelligent Robot ◽  
Level Control ◽  
Level Control System ◽  
Initial Location ◽  
Group Control ◽  
Multi Level

The article is devoted to the application of a group of robotic complexes for military purposes. The current state of control systems of single robotic complexes does not allow solving all the tasks assigned to the robot. The analysis of methods of controlling a group of robots in combat conditions is carried out. The necessity of using a multi-level control system for an intelligent combat robot is justified. A multi-level control system for an intelligent robot is proposed. Such a system assumes the possibility of controlling the robot in one of four modes: remote, supervisory, autonomous and group. Moreover, each robot, depending on the external conditions and its condition, can be in any control mode. The application of the technique is shown by the example of the movement of a group of robots with an interval along the front. The problem of the movement of slave robots behind the leader is considered. When forming the robot control algorithm, the method of finite automata was used. The algorithm controls the movement of the RTK in various operating modes: group control mode and autonomous movement mode. In the group control mode, the task is implemented: movement for the leader. For the state of "Movement in formation", an algorithm for forming the trajectory of the movement of guided robots was implemented. An algorithm for approximating the Bezier curve was used. It allows you to build a trajectory for the slave robot. On the basis of the obtained trajectory, the angular and linear velocity were calculated. In the autonomous control mode, two tasks are solved: moving to a given point and avoiding obstacles. Vector Field Histogram was used as an algorithm for detouring an obstacle, which determines the direction of movement without obstacles. The state of "Movement to a given point" is based on Pure Pursuit as a simple and reliable algorithm for solving such problems. A computer model of the movement of a group of robots was developed. The model is implemented in the MATLAB program using the Simulink and Mobile Robotics Simulation Toolbox libraries. Several different variants of the movement of the RTK group are modeled, which differ from each other in the initial location of the robots and the position of obstacles. The conducted computer simulation showed the efficiency and effectiveness of the proposed method of RTC control.

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